The present invention relates to a defect classify and inspection method, and apparatus, to classify defects such as minute pattern defects and dust particles on the basis of an image of an inspection subject obtained from a thin film device such as a semiconductor wafer, a TFT or a photomask by using lamp light, laser light or an electron beam. In particular, the present invention relates to a defect inspection method, and apparatus, suitable for inspecting defects in a semiconductor wafer.
Thin film devices such as semiconductor wafers, liquid crystal displays and hard disk magnetic heads are manufactured through a large number of working processes. In the manufacture of such thin film devices, visual inspection is executed every some series of processes with the object of yield improvement and stabilization. In the visual inspection, defects such as pattern defects or dust particles are detected on the basis of a reference image and an inspection image obtained respectively from corresponding regions of two patterns formed originally so as to have the same shape by using lamp light, laser light or an electron beam. In other words, the reference image is aligned with the inspection image to calculate a difference, and the difference is compared with a separately determined threshold to detect a part having a large difference as a defect or a dust particle. At the same time, features, such as the luminance and size, of a defect are calculated from an image of the defect part and the defect is sorted on the basis of the features, in some cases.
For example, an inspection apparatus that sorts a dust particle which is a convex defect and a scratch which is a concave defect according to a difference in scattered light intensity caused by vertical illumination and oblique illumination is disclosed in JP-A-2002-257533 (Patent Document 1). When determining a defect sort condition of the inspection apparatus having such a defect sort function, it is necessary to instruct a class to be sorted into by using a review and derive a relation between features and the class. In the above-described example, the class to be sorted into is either a dust particle or a scratch. The scattered light intensity under the vertical illumination and the scattered light intensity under the oblique illumination are used as the features. A discriminant line is set manually on the basis of a two-dimensional scatter diagram.
In addition, there are instruction type and rule base type in sort techniques. In the instruction type, a sorter is automatically constructed by instruction of feature data associated with a correct answer class. In a method used in sort of the instruction type, a defect is sorted into a class of already taught defects having a shortest distance in the feature space. In another method used in sort of the instruction type, feature distribution of each defect class is presumed on the basis of instruction data and a defect is sorted into a class in which the occurrence probability of the features of a defect to be sorted is the highest. The rule base type is a method of sorting defects according to a rule described in the “if-then-else” form. In many cases, the rule is represented by a threshold for a feature. The classify method described in Patent Document 1 is also a kind of the rule base type.
A method for generating a defect classifier described in JP-A-2004-47939 (Patent Document 2) includes an inspection information acquisition step of inspecting a defective sample group on an arbitrary sample by using at least an arbitrary defect inspection apparatus and acquiring sample inspection information, and a decision tree setting step. The decision tree setting step includes a display step of displaying a state of defect attribute distribution of a defective sample group on the arbitrary sample, on a screen on the basis of the sample inspection information acquired at the inspection information acquisition step. The decision tree setting step further includes a classify rule setting step of setting an individual classify rule for each of branch elements in a decision tree, which hierarchically develops sort class elements of the defective sample group via branch elements, on the basis of the state of the defect attribute distribution displayed on the screen.